Long only 1/n portfolio

import pandas as pd
pd.options.plotting.backend = "plotly"

import yfinance as yf

from cvx.simulator.builder import builder
from cvx.simulator.grid import resample_index

data = yf.download(tickers = "SPY AAPL GOOG MSFT",  # list of tickers
                   period = "10y",                   # time period
                   interval = "1d",                 # trading interval
                   prepost = False,                 # download pre/post market hours data?
                   repair = True)                   # repair obvious price errors e.g. 100x?

[                       0%                       ]

[**********************50%                       ]  2 of 4 completed

[**********************75%***********            ]  3 of 4 completed

[*********************100%***********************]  4 of 4 completed

prices = data["Adj Close"]

capital = 1e6

b = builder(prices=prices, initial_cash=capital)

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    b[time[-1]] = 0.25 * state.nav / state.prices

portfolio = b.build()
portfolio.profit.cumsum().plot()

portfolio.nav.plot()

Rebalancing

Usually we would not execute on a daily basis but rather rebalance every week, month or quarter. There are two approaches to deal with this problem in cvxsimulator.

• Resample the existing daily portfolio (helpful to see effect of your hesitated trading)

• Trade only on days that are within a predefined grid (most flexible if you have a rather irregular grid)

Resample an existing portfolio

portfolio_resampled = portfolio.resample(rule="M")

frame = pd.DataFrame({"original": portfolio.nav, "monthly": portfolio_resampled.nav})
frame

	original	monthly
Date
2013-07-05	1.000000e+06	1.000000e+06
2013-07-08	1.004129e+06	1.004129e+06
2013-07-09	1.010539e+06	1.010499e+06
2013-07-10	1.012445e+06	1.012388e+06
2013-07-11	1.031042e+06	1.030990e+06
...	...	...
2023-06-27	8.007722e+06	7.991767e+06
2023-06-28	8.063879e+06	8.046463e+06
2023-06-29	8.052829e+06	8.036507e+06
2023-06-30	8.172189e+06	8.156990e+06
2023-07-03	8.136412e+06	8.121082e+06

2516 rows × 2 columns

print(portfolio_resampled.stocks)

                    AAPL          GOOG         MSFT          SPY
Date                                                            
2013-07-05  19315.524378  11234.017461  8769.551127  1842.407166
2013-07-08  19315.524378  11234.017461  8769.551127  1842.407166
2013-07-09  19315.524378  11234.017461  8769.551127  1842.407166
2013-07-10  19315.524378  11234.017461  8769.551127  1842.407166
2013-07-11  19315.524378  11234.017461  8769.551127  1842.407166
...                  ...           ...          ...          ...
2023-06-27  10994.634333  15920.427861  5953.526179  4711.437650
2023-06-28  10994.634333  15920.427861  5953.526179  4711.437650
2023-06-29  10994.634333  15920.427861  5953.526179  4711.437650
2023-06-30  10994.634333  15920.427861  5953.526179  4711.437650
2023-07-03  10568.964027  16872.121174  6018.234109  4583.480584

[2516 rows x 4 columns]

# almost hard to see that difference between the original and resampled portfolio
frame.plot()

# number of shares traded
portfolio_resampled.trades_stocks.iloc[1:].plot()

Trade only days in predefined grid

b = builder(prices=prices, initial_cash=capital)

# define a grid
grid = resample_index(prices.index, rule="M")

for time, state in b:
    # each day we invest a quarter of the capital in the assets
    if time[-1] in grid:
        b[time[-1]] = 0.25 * state.nav / state.prices
    else:
        # forward fill an existing position
        b[time[-1]] = b[time[-2]]
        
portfolio = b.build()

portfolio.nav.plot()

# Trading only once a month can lead to days where 150k had to be reallocated
portfolio.turnover.iloc[1:].plot()

Why not resampling the prices?

I don’t believe in bringing the prices to a monthly grid. This would render it hard to construct signals given the sparse grid. We stay on a daily grid and trade once a month.